# Effect of Input Noise Dimension in GANs

arXiv: https://arxiv.org/abs/2004.06882 · HTML: https://mlsanity.com/paper/2004.06882/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion Models](https://mlsanity.com/paper/2311.14097/) (2023)
- [Generative Adversarial Networks and Image-Based Malware Classification](https://mlsanity.com/paper/2207.00421/) (2022)
- [Finding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networks](https://mlsanity.com/paper/2211.15936/) (2022)
- [3D GANs and Latent Space: A comprehensive survey](https://mlsanity.com/paper/2304.03932/) (2023)
- [Noise Dimension of GAN: An Image Compression Perspective](https://mlsanity.com/paper/2403.09196/) (2024)
- [LCIP: Loss-Controlled Inverse Projection of High-Dimensional Image Data](https://mlsanity.com/paper/2602.11141/) (2026)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) are by far the most successful generative models. Learning the transformation which maps a low dimensional input noise to the data distribution forms the foundation for GANs. Although they have been applied in various domains, they are prone to certain challenges like mode collapse and unstable training. To overcome the challenges, researchers have proposed novel loss functions, architectures, and optimization methods. In our work here, unlike the previous approaches, we focus on the input noise and its role in the generation. We aim to quantitatively and qualitatively study the effect of the dimension of the input noise on the performance of GANs. For quantitative measures, typically emphFr'echet Inception Distance (FID) and Inception Score (IS) are used as performance measure on image data-sets. We compare the FID and IS values for DCGAN and WGAN-GP. We use three different image data-sets -- each consisting of different levels of complexity. Through our experiments, we show that the right dimension of input noise for optimal results depends on the data-set and architecture used. We also observe that the state of the art performance measures does not provide enough useful insights. Hence we conclude that we need further theoretical analysis for understanding the relationship between the low dimensional distribution and the generated images. We also require better performance measures.